Catálogo de publicaciones - libros

Geoid determination is nowadays an important scientific problem in the fields of Geosciences. Ellipsoidal and orthometric heights are commonly used height systems in geodesy. Ellipsoidal height, measured from satellite such as GPS and GLONASS, is reckoned from ellipsoid. On the other hand orthometric height is measured from geoid. Although orthometric height has physical meaning, ellipsoidal height has just mathematical definition. Geoid height is a transformation parameter between these heights systems and a tool for rational usage of coordinates obtained from satellite measurements. Fuzzy logic theory has been popular in many different scientific, engineering fields and many geodetic problems have been solved by using fuzzy logic recently. In this study, theory and how to calculate geoid height by Fuzzy logic using Matlab is explained and a case study in Burdur (Turkey) is performed. Calculations are interpreted, discussed and conclusion is drawn.

In the paper a modification of greedy algorithm with weights for construction of partial covers is considered. Theoretical and experimental results relating to accuracy of this algorithm are discussed.

In a 1976 Dempster and Shafer have created a mathematical theory of evidence called Dempster-Shafer theory. This theory is based on belief functions and plausible reasoning, which is used to combine separate pieces of information (evidence) to calculate the probability of an event. In 1982 Pawlak has created the rough set theory as an innovative mathematical tool to describing the knowledge, including also the uncertain and inexact knowledge. In 1994 the basic functions of the evidence theory have been defined, based on the notion from the rough set theory. This dependence between these theories has allowed further research on their practical usage.

In this paper the problem of long and not quite efficient inference process is considered. There is some problem with large set of data, e.g. set of rules, which causes long time of inference process. The paper present the idea of hierarchical structure of knowledge base, where on each level of hierarchy there are created some groups of similar rules. The cluster analysis method has been used to build clusters of rules. Then, the interpreter of rules want be searching set of rules step by step (one by one). It has to be founded the most similar group of rules and all inference processes working on this small (exact) set of rules.

The paper presented hereunder pays attention to discussion of the method of using the Petri nets as the modelling tool of the processes occurring during inference. This issue is a part of the project concerning the extension of decision units model to the possibilities of effective detection and visualisation of knowledge base verification results. The basic terms of Petri nets as well as the idea of using Petri nets as the modelling agent of rule knowledge base have been presented in this paper. The method of using Petri nets for modelling of the inference process has been also discussed in further part of this paper. Short discourse has been included in the summary to this paper on foreseen directions of Petri nets usage in verification of dynamic properties of rule knowledge bases as well as the possibilities of using Petri nets for extending the properties of decision units.

This paper presents a conception of fast and useful inference process in knowledge based systems. The main known weakness is long and not smart process of looking for rules during the inference process. Basic inference algorithm, which is used by the rule interpreter, tries to fit the facts to rules in knowledge base. So it takes each rule and tries to execute it. As a result we receive the set of new facts, but it often contains redundant information unexpected for user. The main goal of our works is to discover the methods of inference process controlling, which allow us to obtain only necessary decision information. The main idea of them is to create rules partitions, which can drive inference process. That is why we try to use the hierarchical clustering to agglomerate the rules.

Many design problems can be faced with large amount of information and uncertainty that in consequence lead to the large number of problem states, parameters and dependencies between them. Therefore, it is often hardly possible to model the problem in symbolical form using the domain knowledge or to find acceptable solution on the basis of it. In many practical problems there is a requirement for the decision support system to opearte in a dynamically changing environment. The system has to deal with continues data flow, beeing self situated in spatio-temporal environment. In such cases, it could be considered to apply AI techniques and machine learning methods. In this paper we propose an approach that aims to respond to this challenge by the construction of a learning system based on multiagent paradigm. The focus of the paper concentrates on a singleagent level where the local lazy learning method has been analysed. The results of the experiments indicate the satisfactory efficiency of the proposed solution.

This paper presents results obtained in experiments related to artificial neural networks. Artificial neural networks have been trained with delta-bar-delta and conjugate gradient algorithms in case of removing some data from dataset and fulfilling empty places with mean. The goal of the experiment was to observe how long will neural network (trained with specific algorithm) be able to learn when dataset will be consistently less and less exact – the number of incomplete data is increased.

Negative selection is an immune-inspired algorithm which is typically applied to anomaly detection problems. We present an empirical investigation of the generalization capability of the Hamming negative selection, when combined with the r-chunk affinity metric. Our investigations reveal that when using the r-chunk metric, the length is a crucial parameter and is inextricably linked to the input data being analyzed. Moreover, we propose that input data with different characteristics, i.e. different positional biases, can result in an incorrect generalization effect.

We first establish the potential usefulness of simulation in immunological research, and then explore some of the problems that are preventing its widespread use. We suggest solutions for each of these problems, and illustrate both problems and solutions with an example from our own research – an experiment that tests a novel theory of immunological memory, in which our simulation effectively closed the experiment-theorise loop.